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Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial. Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving.

Driveline plunge mechanism dynamics has a significant contribution to the driver's perceivable transient NVH error states and to the transmission shift quality. As it accounts for the pitch or roll movements of the front powerplant and rear drive unit, the plunging joints exhibit resisting force in the fore-aft direction under various driveline torque levels. This paper tackles the difficult task of quantifying the coefficient of static friction and the coefficient of dynamic friction in a simple to use metric as it performs in the vehicle. The comparison of the dynamic friction to the static friction allows for the detection of the occurrence of stick-slip in the slip mechanism; which enables for immediate determination of the performance of the design parameters such as spline geometry, mating parts fit and finish, and lubrication. It also provides a simple format to compare a variety of designs available to the automotive design engineer.

As the world's largest auto producer and consumer, China is both the most promising and complex market given the country's rapid economic growth, huge population, and many regional and segment preference differences. This research is aimed at developing data-driven demand models for customer preference analysis and prediction under a competitive market environment. Regional analysis is first used to understand the impact of geographical factors on customer preference. After a comprehensive data exploration, a customer-level mixed logit model is built to shed light on fast-growing vehicle segments in the Chinese auto market. By combining the data of vehicle purchase, consideration, and past choice, cross-shopping behaviors and brand influence are explicitly modeled in addition to the impact of customer demographics, usage behaviors, and attributes of vehicles.

This paper proposes an approach to determine driver's driving behavior, style or habit during vehicle handling maneuvers and heavy traction and braking events in real-time. It utilizes intelligence inferred from driver's control inputs, vehicle dynamics states, measured signals, and variables processed inside existing control modules such as those of anti-lock braking, traction control, and electronic stability control systems. The algorithm developed for the proposed approach has been experimentally validated and shows the effectiveness in characterizing driver's handling behavior. Such driver behavior can be used for personalizing vehicle electronic controls, driver assistant and active safety systems, and the other vehicle control features.

Injury distributions of belted drivers in 1998-2013 model-year light passenger cars/trucks in various types of real-world frontal crashes were studied. The basis of the analysis was field data from the National Automotive Sampling System (NASS). The studied variables were injury severity (n=2), occupant body region (n=8), and crash type (n=8). The two levels of injury were moderate-to-fatal (AIS2+) and serious-to-fatal (AIS3+). The eight body regions ranged from head/face to foot/ankle. The eight crash types were based on a previously-published Frontal Impact Taxonomy (FIT). The results of the study provided insights into the field data. For example, for the AIS2+ upper-body-injured drivers, (a) head and chest injury yield similar contributions, and (b) about 60% of all the upper-body injured drivers were from the combination of the Full-Engagement and Offset crashes.

Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. Past studies of lane change detection have relied on vehicular data, such as steering angle, velocity, and acceleration. In this paper, we use three physiological signals from the driver to detect lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel system is proposed which uses a Granger causality test for feature selection and a neural network for classification. Test results showed that for 30 lane change events and 60 non lane change events in on-the-road driving, a true positive rate of 70% and a false positive rate of 10% was obtained.

Dynamic vehicle loads play critical roles for automotive controls including battery management, transmission shift scheduling, distance-to-empty predictions, and various active safety systems. Accurate real-time estimation of vehicle loads such as those due to vehicle mass and road grade can thus improve safety, efficiency, and performance. While several estimation methods have been proposed in literature, none have seen widespread adoption in current vehicle technologies despite their potential to significantly improve automotive controls. To understand and bridge the gap between research development and wider adoption of real-time load estimation, this paper assesses the accuracy and performance of four estimation methods that predict vehicle mass and/or road grade.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

The objectives of this study were a) to determine how expert judges categorized valid Integrated Vehicle-Based Safety Systems (IVBSS) Forward Collision Warning (FCW) events from review of naturalistic driving data; and b) to determine how consistent these categorizations were across the judges working in pairs. FCW event data were gathered from 108 drivers who drove instrumented vehicles for 6 weeks each. The data included video of the driver and road scene ahead, beside, and behind the vehicle; audio of the FCW alert onset; and engineering data such as speed and braking applications. Six automotive safety experts examined 197 ‘valid’ (i.e., conditions met design intent) FCW events and categorized each according to a taxonomy of primary contributing factors. Results indicated that of these valid FCW events, between 55% and 73% could be considered ‘nuisance alerts’ by the driver.

Thermophysical properties of materials used in the design of automotive interiors are needed for computer simulation of climate conditions inside the vehicle. These properties are required for assessment of the vehicle occupants' thermal sensation as they come in contact with the vehicle interior components, such as steering wheels, arm rests, instruments panel and seats. This paper presents the results of an investigation into the thermophysical properties of materials which are required for solving the non-linear Fourier equations with any boundary conditions and taking into account materials' specific heat, volume density, thermal conductivity, and thermal optical properties (spectral and total emissivity and absorptivity). The model and results of the computer simulation will be published in a separate paper.

Nighttime driving behavior differs from that during the day because of unique scenarios presented in a driver's field of vision. At night drivers have to rely on their vehicle headlamps to illuminate the road to be able to see the environment and road conditions in front of him. In recent decades car illumination systems have undergone considerable technological advances such as the use of a Light Emitting Diode (LED) in Adaptive Front-lighting Systems (AFS), a breakthrough in lighting technology. This is rapidly becoming one of the most important innovative technologies around the world within the lighting community. This paper discusses driver's needs given the environment and road conditions using a survey applied to compare the needs of both truck and car drivers under different road conditions. The results show the potential and suitability of the methodology proposed for controlling truck-related lighting in any emergent market.

Distance to empty (DTE) estimation is an important factor to electric vehicle (EV) applications due to its limited driving range. The DTE calculation is based on available energy of the battery and power usage by the powertrain components (e.g. electric motor) and climate control components (e.g. PTC heater and electric AC compressor). The conventional way of estimating the DTE is to treat the power consumed by the climate control system the same as the power by the powertrain for either instantaneous or rolling average estimation. The analysis in this study shows that the power consumption by the climate control system should be estimated based on the current ambient conditions and driver's input instead of using the recorded data from the past driving cycles. The climate control should also be considered separately from the powertrain in power usage rolling average calculation, which results in improvements in DTE estimation especially for extreme hot and cold conditions.

A transportable instrumentation package to collect driver, vehicle and environmental data is described. This system is an improvement on an earlier system and is called TIP-II [13]. Two new modules were designed and added to the original system: a new and improved physiological signal module (PH-M) replaced the original physiological signals module in TIP, and a new hand pressure on steering wheel module (HP-M) was added. This paper reports on exploratory tests with TIP-II. Driving data were collected from ten driver participants. Correlations between On-Board-Diagnostics (OBD), video data, physiological data and specific driver behavior such as lane departure and car following were investigated. Initial analysis suggested that hand pressure, skin conductance level, and respiration rate were key indicators of lane departure lateral displacement and velocity, immediately preceding lane departure; heart rate and inter-beat interval were affected during lane changes.

This work presents an application of airborne source path contribution analysis with emphasis on prediction of wideband sounds inside a cabin from measurements made around a stand-alone engine. The heart of the method is a time domain source path receiver technique wherein the engine surface is modeled as a number of source points. Nearfield microphone measurements and transfer functions are used to quantify the source strengths at these points. This acoustic engine model is then used in combination with source-to-receiver transfer functions to calculate sound levels at other positions, such as at the driver's ear position. When combining all the data, the in-cabin engine sound can be synthesized even before the engine is physically installed into the vehicle. The method has been validated using a powertrain structure artificially excited by several shakers playing band-limited noise so as to produce a complicated vibration pattern on the surface.

The Reverse Braking Assist (RBA) feature is designed to automatically activate full braking in a backing vehicle. When this feature activates, a backing vehicle is suddenly stopped or may slide to a stop. During this process, an understanding of the driver's behavior may be useful in the design of an appropriate human-machine-interface (HMI) for the RBA. Several experimental studies were done to examine driver behavior in response to an unexpected and automatic braking event while backing [1]. Two of these studies are reported in this paper. A 7-passenger Crossover Utility Vehicle was fitted with a rear-view camera, a center-stack mounted LCD screen, and ancillary recording devices. In the first study, an object was suddenly placed in the path of a backing vehicle. The backing vehicle came to a sudden and complete stop. The visual image of the backing path on the LCD prominently showed that an obstacle was present in the backing path of the vehicle.

Emissions sample probes are widely used in engine and vehicle emissions development testing. Tailpipe bag summary data is used for certification, but the time-resolved (or modal) emissions data at various points along the exhaust system is extremely important in the emission control technology development process. Exhaust gas samples need to be collected at various locations along the exhaust aftertreatment system. Typically, a tube with a small diameter is inserted inside the exhaust pipe to avoid any significant effect on flow distribution. The emissions test equipment draws a gas sample from the exhaust stream at a constant volumetric flow rate (typically around 10 SLPM). The sample probe tube delivers exhaust gas from the exhaust pipe to emissions test equipment through multiple holes on the surface of tube. There can be multiple rows of holes at different axial planes along the length of the sample probe as well as multiple holes on a given axial plane of the sample probe.

This paper reports a study undertaken by the Crash Safety Working Group (CSWG) of the United States Council for Automotive Research (USCAR) to determine generic acceleration pulses for testing and evaluating advanced batteries subjected to inertial loading for application in electric passenger vehicles. These pulses were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used in this study. Crash test data, in terms of acceleration time histories, were collected from various crash modes conducted by the National Highway Traffic Safety Administration (NHTSA) during their New Car Assessment Program (NCAP) and Federal Motor Vehicle Safety Standards (FMVSS) evaluations, and the Insurance Institute for Highway Safety (IIHS).

Torque controls for the engine and electric motors in a Powersplit HEV are keys to the success of balancing fuel economy, driveability, and battery power control. The electric variable transmission (EVT) offers an opportunity to let the engine operate at system-optimal fuel efficient points independently of any load. Existing work shows such a benefit can be realized through a decentralized control structure that translates the driver inputs to independent engine torque and speed control. However, our study shows that the decentralized control structures have a fundamental limitation that arises from the nonminimum phase (NMP) zero in the transfer function from the driver power command to the generator torque change rate, and thus not only is it difficult to obtain smooth generator torque but also it can cause violations on battery power limits during transients. Additionally, it adversely affects the driveability due to the generator torque transients reflected at the ring gear.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of parasitic losses on all driveline components is required. A standardized comparison procedure enables manufacturers and suppliers to measure component losses consistently, in addition to offering a reliable process to assess enablers for efficiency improvements. This paper reviews the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This procedure was validated for repeatability considering variations in soak time, temperature measurement positions on the transfer case, and test operating conditions. Additional assessments of spin loss at low ambient temperatures, and the effect of component break-in on spin loss were also conducted.